SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 651–660 of 3874 papers

TitleStatusHype
RealViformer: Investigating Attention for Real-World Video Super-ResolutionCode2
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network—0
MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby ReferencesCode1
UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt—0
Toward INT4 Fixed-Point Training via Exploring Quantization Error for Gradients—0
Leveraging Segment Anything Model in Identifying Buildings within Refugee Camps (SAM4Refugee) from Satellite Imagery for Humanitarian OperationsCode0
Deconvolution with a Box—0
Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse ProblemsCode0
2D Neural Fields with Learned Discontinuities—0
Backdoor Attacks against Image-to-Image Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified